Gradient Descent in Logistic Regression | Complete Derivation Explained | In Hindi

In this video, we explore the step-by-step derivation of Gradient Descent in the context of Logistic Regression. We begin with an introduction to the key concepts, including Maximum Likelihood Estimation, the Loss Function, and the Chain Rule. Additionally, we break down the derivative of the Sigmoid function and complete the full derivation of Gradient Descent.

📌 Video Chapters:

0:00 - Introduction to Steps in Gradient Descent
1:56 - Overview of Maximum Likelihood Estimation
5:10 - Derivation of Gradient Descent for the Logistic Function
7:12 - Detailed Breakdown of the Loss Function
10:48 - Chain Rule in Gradient Descent
11:55 - Derivative of the Sigmoid Function
18:37 - Final Steps in the Derivation of Gradient Descent for Logistic Regression

🎓 Meet Your Instructor
Pritam Kudale, an experienced AI educator with 10+ years of expertise, is dedicated to equipping educators with modern AI/ML teaching strategies and tools.

🔗 Connect with Pritam Kudale: LinkedIn Profile https://www.linkedin.com/in/pritam-kudale-90793236/

🔗 Relevant Links:
Check out more resources in the description below for datasets, code snippets, and additional reading material :
https://github.com/pritkudale/ML-for-Teachers/blob/main/Linear%20Regression/Linear_Regression_case_study_with_outliers.ipynb
https://github.com/pritkudale/Code_for_LinkedIn/blob/main/Linear%20Regression/Linear_Regression_case_study_without_outliers.ipynb

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Видео Gradient Descent in Logistic Regression | Complete Derivation Explained | In Hindi канала Vizuara
GradientDescent, MachineLearning, LogisticRegression, DeepLearning, MLTutorial, AI, ArtificialIntelligence, DataScience, Backpropagation, Optimization, LossFunction, SigmoidFunction, Mathematics, Statistics, MLAlgorithms, Probability, ChainRule, MaximumLikelihood, Derivation, MathematicalOptimization